193 lines
8.4 KiB
Python
193 lines
8.4 KiB
Python
import argparse
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import logging
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import os
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import sys
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import sqlite3
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import pandas as pd
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import json
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from datetime import datetime, timezone
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# Assuming logging_utils.py is in the same directory
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from logging_utils import setup_logging
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class Resampler:
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"""
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Reads new 1-minute candle data from the SQLite database, resamples it to
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various timeframes, and appends the new candles to the corresponding tables.
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"""
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def __init__(self, log_level: str, coins: list, timeframes: dict):
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setup_logging(log_level, 'Resampler')
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self.db_path = os.path.join("_data", "market_data.db")
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self.status_file_path = os.path.join("_data", "resampling_status.json")
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self.coins_to_process = coins
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self.timeframes = timeframes
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self.aggregation_logic = {
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'open': 'first',
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'high': 'max',
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'low': 'min',
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'close': 'last',
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'volume': 'sum',
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'number_of_trades': 'sum'
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}
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self.resampling_status = self._load_existing_status()
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self.job_start_time = None
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def _load_existing_status(self) -> dict:
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"""Loads the existing status file if it exists, otherwise returns an empty dict."""
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if os.path.exists(self.status_file_path):
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try:
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with open(self.status_file_path, 'r', encoding='utf-8') as f:
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logging.debug(f"Loading existing status from '{self.status_file_path}'")
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return json.load(f)
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except (IOError, json.JSONDecodeError) as e:
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logging.warning(f"Could not read existing status file. Starting fresh. Error: {e}")
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return {}
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def run(self):
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"""
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Main execution function to process all configured coins and update the database.
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"""
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self.job_start_time = datetime.now(timezone.utc)
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logging.info(f"--- Resampling job started at {self.job_start_time.strftime('%Y-%m-%d %H:%M:%S %Z')} ---")
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if not os.path.exists(self.db_path):
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logging.error(f"Database file '{self.db_path}' not found.")
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return
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with sqlite3.connect(self.db_path) as conn:
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conn.execute("PRAGMA journal_mode=WAL;")
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logging.debug(f"Processing {len(self.coins_to_process)} coins...")
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for coin in self.coins_to_process:
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source_table_name = f"{coin}_1m"
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logging.debug(f"--- Processing {coin} ---")
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try:
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# Load the full 1m history once per coin
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df_1m = pd.read_sql(f'SELECT * FROM "{source_table_name}"', conn, parse_dates=['datetime_utc'])
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if df_1m.empty:
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logging.warning(f"Source table '{source_table_name}' is empty. Skipping.")
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continue
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df_1m.set_index('datetime_utc', inplace=True)
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for tf_name, tf_code in self.timeframes.items():
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target_table_name = f"{coin}_{tf_name}"
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logging.debug(f" Updating {tf_name} table...")
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last_timestamp = self._get_last_timestamp(conn, target_table_name)
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# Get the new 1-minute data that needs to be processed
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new_df_1m = df_1m[df_1m.index > last_timestamp] if last_timestamp else df_1m
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if new_df_1m.empty:
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logging.debug(f" -> No new 1-minute data for {tf_name}. Table is up to date.")
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continue
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resampled_df = new_df_1m.resample(tf_code).agg(self.aggregation_logic)
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resampled_df.dropna(how='all', inplace=True)
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if not resampled_df.empty:
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# Append the newly resampled data to the target table
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resampled_df.to_sql(target_table_name, conn, if_exists='append', index=True)
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logging.debug(f" -> Appended {len(resampled_df)} new candles to '{target_table_name}'.")
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if coin not in self.resampling_status: self.resampling_status[coin] = {}
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total_candles = int(self._get_table_count(conn, target_table_name))
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self.resampling_status[coin][tf_name] = {
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"last_candle_utc": resampled_df.index[-1].strftime('%Y-%m-%d %H:%M:%S'),
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"total_candles": total_candles
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}
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except Exception as e:
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logging.error(f"Failed to process coin '{coin}': {e}")
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self._log_summary()
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self._save_status()
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logging.info(f"--- Resampling job finished at {datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M:%S %Z')} ---")
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def _log_summary(self):
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"""Logs a summary of the total candles for each timeframe."""
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logging.info("--- Resampling Job Summary ---")
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timeframe_totals = {}
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# Iterate through coins, skipping metadata keys
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for coin, tfs in self.resampling_status.items():
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if not isinstance(tfs, dict): continue
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for tf_name, tf_data in tfs.items():
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total = tf_data.get("total_candles", 0)
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if tf_name not in timeframe_totals:
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timeframe_totals[tf_name] = 0
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timeframe_totals[tf_name] += total
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if not timeframe_totals:
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logging.info("No candles were resampled in this run.")
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return
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logging.info("Total candles per timeframe across all processed coins:")
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for tf_name, total in sorted(timeframe_totals.items()):
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logging.info(f" - {tf_name:<10}: {total:,} candles")
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def _get_last_timestamp(self, conn, table_name):
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"""Gets the timestamp of the last entry in a table."""
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try:
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return pd.read_sql(f'SELECT MAX(datetime_utc) FROM "{table_name}"', conn).iloc[0, 0]
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except (pd.io.sql.DatabaseError, IndexError):
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return None
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def _get_table_count(self, conn, table_name):
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"""Gets the total row count of a table."""
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try:
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return pd.read_sql(f'SELECT COUNT(*) FROM "{table_name}"', conn).iloc[0, 0]
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except (pd.io.sql.DatabaseError, IndexError):
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return 0
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def _save_status(self):
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"""Saves the final resampling status to a JSON file."""
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if not self.resampling_status:
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return
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stop_time = datetime.now(timezone.utc)
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self.resampling_status['job_start_time_utc'] = self.job_start_time.strftime('%Y-%m-%d %H:%M:%S')
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self.resampling_status['job_stop_time_utc'] = stop_time.strftime('%Y-%m-%d %H:%M:%S')
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# Clean up old key if it exists from previous versions
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self.resampling_status.pop('last_completed_utc', None)
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try:
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with open(self.status_file_path, 'w', encoding='utf-8') as f:
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json.dump(self.resampling_status, f, indent=4, sort_keys=True)
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logging.info(f"Successfully saved resampling status to '{self.status_file_path}'")
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except IOError as e:
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logging.error(f"Failed to write resampling status file: {e}")
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def parse_timeframes(tf_strings: list) -> dict:
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"""Converts a list of timeframe strings into a dictionary for pandas."""
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tf_map = {}
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for tf_str in tf_strings:
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numeric_part = ''.join(filter(str.isdigit, tf_str))
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unit = ''.join(filter(str.isalpha, tf_str)).lower()
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code = ''
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if unit == 'm': code = f"{numeric_part}min"
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elif unit == 'w': code = f"{numeric_part}W"
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elif unit in ['h', 'd']: code = f"{numeric_part}{unit}"
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else: code = tf_str
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tf_map[tf_str] = code
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return tf_map
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Resample 1-minute candle data from SQLite to other timeframes.")
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parser.add_argument("--coins", nargs='+', required=True, help="List of coins to process.")
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parser.add_argument("--timeframes", nargs='+', required=True, help="List of timeframes to generate.")
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parser.add_argument("--log-level", default="normal", choices=['off', 'normal', 'debug'])
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args = parser.parse_args()
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timeframes_dict = parse_timeframes(args.timeframes)
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resampler = Resampler(log_level=args.log_level, coins=args.coins, timeframes=timeframes_dict)
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resampler.run()
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